Financial institutions and businesses involved with sales of property have long tried to estimate values of property accurately. Accurate estimation serves many important purposes. For example, financial institutions use property value estimates as one of the key factors in calculating the loan to value (LTV) ratio of a home. The LTV ratio is the ratio of a first mortgage to the appraised value of the real property. The LTV ratio is an important calculation used by financial institutions to assess lending risks. For example, as the LTV ratio of a property increases, the likelihood of loan default increases. In addition, when a default does occur, the higher the LTV ratio, the greater the potential financial loss to the financial institution. Moreover, financial institutions may use the LTV ratio to mark-to-market their portfolio of outstanding loans. Mark-to-market is an accounting methodology used to calculate current LTV ratio of outstanding loans. Accordingly, the accuracy of the estimated value of real estate needed to calculate the LTV ratio is critical.
One technique for attempting to obtain an accurate estimated value of real estate utilizes a repeat sales index. A repeat sales index may be used to identify housing market conditions and the amount of equity homeowners have gained through house price appreciation. The index itself is a composite of changes for individual house prices within a geographical region, such as a municipality, zip code, county, region, or state. The data used in the repeat sales index may comprise successive selling prices and the sale dates for the same property (e.g., residential house). In essence, this approach finds the average rate of property appreciation in each period that gives the best statistical fit to all the overlapping holding periods. By using pricing of the same property, the repeat sales index eliminates the bias in price changes that are not due to the true house price change, but due to external factors such as, for example, consumer trends for bigger houses.
The basic repeat sales index may be improved through the use of data from refinance transactions, in addition to data from purchase transactions, in forming repeat sales forecasts, thereby increasing the size of the estimation sample and the timeliness of the evaluation sample. Moreover, as disclosed in U.S. Pat. No. 6,401,070, the data used in a repeat sales index may be weighted to provide particular importance to one set of data over another. The content of U.S. Pat. No. 6,401,070 is incorporated herein by reference.
There are qualitative differences between house price data derived from purchase transactions and from refinance transactions. Purchase transactions typically involve arms-length agreements in which the incentives of the parties will tend to result in an unbiased sales price, and the information of the three parties (buyer, seller, and appraiser) will tend to result in greater accuracy in ascertaining the value of the property. Refinance transactions, on the other hand, have valuation based solely on an appraisal and consequently are subject to several sources of bias. For example, incentive biases in appraisals arise because appraisers are motivated to arrive at valuations that can make the refinance transaction successful. Selection biases arise because, particularly in a down market, the properties that are eligible for refinance are more likely to be those that have appreciated relative to the market as a whole. Accordingly, a repeated sales index that factors in biases to the data is referred to as a weighted repeat sales index (WRSI). Here WRSI is used generically. WRSI also refers to indexes that include refinance transactions as well as property sale transactions, and indexes with and without weights on the transactions. As disclosed in U.S. Pat. No. 6,401,070, the WRSI may be expressed as:log(Ps/Pt)=Is−It+ds2Rs2−dt1Rt1+ξ
The variable Pt is the first transaction price, Ps is the second transaction price, It is the log index value at time t, Rt1 is equal to one (1) if the first transaction is a refinance and equal to zero (0) otherwise, Rs2 is equal to one (1) if the second transaction is a refinance and equal to zero (0) otherwise, dt1 is a coefficient representing the first transaction refinance bias at time t, ds2 is coefficient representing the second transaction refinance bias at time s, and ξ is the error term. In essence, the refinance bias terms measure the difference in appreciation between purchase and refinance transactions at the two dates. The dt1 coefficients may be thought of as measuring the incentive bias and the ds2 coefficients as measuring the combined selection and incentive bias. Accordingly the WRSI model of equation (1) allows for time varying differences between refinance and purchase transactions, thereby improving forecast accuracy.
As used herein, “aggregated level” refers to a geographic region comprised of smaller geographic regions. For example, a state may be an aggregated level of counties. As used herein, “disaggregated level” refers to a geographic region that may be included in an aggregated level. For example, a county may be a disaggregated level of a state.
Using the WRSI model, trends and growth changes in house prices may be evident from examining plots of quarterly WRSI growth at aggregated levels, such as a state level and region level (e.g., one of the geographic regions within the United States of America officially recognized by the United States Census Bureau). However, trends and growth changes in house prices are generally not evident when examining quarterly plots of WRSI growth at disaggregated levels, such as at a county level, a zip code level, or a census tract. This disparity is caused primarily by the occurrence of relative fewer transactions (i.e., purchases, refinances) at a disaggregated level when compared to an aggregated level. Accordingly, when examining WRSI at a disaggregated level, a proper analysis of market conditions and trends is not possible over relatively short periods of time. For example, in a volatile housing market, housing prices in a large geographic region, such as the state of California, may fall in one quarter by 10%. However, examining the WRSI within a zip code in California over the same quarterly or monthly period may not demonstrate a fall of 10%. This is due to the relative lower number of transactions at the zip code level. In fact, there may be very few, or even no transactions in the zip code in the quarter or month.
Accordingly, the inventors have determined that WRSI may lag in providing property growth rate estimates at disaggregated geographic levels, and may not exhibit seasonal differences in property values. Systems and methods consistent with the present invention address the difficulties discussed above by providing an adjusted WRSI that calculates a more accurate estimated value of real estate growth rates at disaggregated levels, among other things.